@inproceedings{liu-etal-2025-lore,
title = "{L}o{RE}-Merging: Exploring Low-Rank Estimation For Large Language Model Merging",
author = "Liu, Zehua and
Wu, Han and
Yao, Yuxuan and
Fu, Xiaojin and
She, Ruifeng and
Han, Xiongwei and
Zhong, Tao and
Yuan, Mingxuan",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1195/",
pages = "21919--21926",
ISBN = "979-8-89176-335-7",
abstract = "While most current approaches rely on further training techniques, such as fine-tuning or reinforcement learning, to enhance model capacities, model merging stands out for its ability of improving models without requiring any additional training. In this paper, we propose a unified framework for model merging based on low-rank estimation of task vectors without the need for access to the base model, named LoRE-Merging. Our approach is motivated by the observation that task vectors from fine-tuned models frequently exhibit a limited number of dominant singular values, making low-rank estimations less prone to interference. We implement the method by formulating the merging problem as an optimization problem. Extensive empirical experiments demonstrate the effectiveness of our framework in mitigating interference and preserving task-specific information, thereby advancing the state-of-the-art performance in model merging techniques."
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<abstract>While most current approaches rely on further training techniques, such as fine-tuning or reinforcement learning, to enhance model capacities, model merging stands out for its ability of improving models without requiring any additional training. In this paper, we propose a unified framework for model merging based on low-rank estimation of task vectors without the need for access to the base model, named LoRE-Merging. Our approach is motivated by the observation that task vectors from fine-tuned models frequently exhibit a limited number of dominant singular values, making low-rank estimations less prone to interference. We implement the method by formulating the merging problem as an optimization problem. Extensive empirical experiments demonstrate the effectiveness of our framework in mitigating interference and preserving task-specific information, thereby advancing the state-of-the-art performance in model merging techniques.</abstract>
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%0 Conference Proceedings
%T LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging
%A Liu, Zehua
%A Wu, Han
%A Yao, Yuxuan
%A Fu, Xiaojin
%A She, Ruifeng
%A Han, Xiongwei
%A Zhong, Tao
%A Yuan, Mingxuan
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F liu-etal-2025-lore
%X While most current approaches rely on further training techniques, such as fine-tuning or reinforcement learning, to enhance model capacities, model merging stands out for its ability of improving models without requiring any additional training. In this paper, we propose a unified framework for model merging based on low-rank estimation of task vectors without the need for access to the base model, named LoRE-Merging. Our approach is motivated by the observation that task vectors from fine-tuned models frequently exhibit a limited number of dominant singular values, making low-rank estimations less prone to interference. We implement the method by formulating the merging problem as an optimization problem. Extensive empirical experiments demonstrate the effectiveness of our framework in mitigating interference and preserving task-specific information, thereby advancing the state-of-the-art performance in model merging techniques.
%U https://aclanthology.org/2025.findings-emnlp.1195/
%P 21919-21926
Markdown (Informal)
[LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging](https://aclanthology.org/2025.findings-emnlp.1195/) (Liu et al., Findings 2025)
ACL
- Zehua Liu, Han Wu, Yuxuan Yao, Xiaojin Fu, Ruifeng She, Xiongwei Han, Tao Zhong, and Mingxuan Yuan. 2025. LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 21919–21926, Suzhou, China. Association for Computational Linguistics.